What is label smoothing and why is it used in vision classification models?

Label smoothing softens hard labels to improve generalization and reduce overconfidence.

Instead of assigning probability 1.0 to correct class and 0.0 to others, label smoothing assigns a small probability to all classes. This prevents the model from becoming overconfident and improves calibration, especially in deep networks like transformers.